Creating Effective Cryptocurrency Trading Algorithms

Thu May 04 2023

Finding, defining and refining an algorithmic trading strategy is a process that takes time and needs to be thorough. Your algorithmic trading strategy is only as good as you programmed it to be. Here's are some best practices to follow.

Algorithmic trading has become increasingly popular among both institutional and retail traders. Even if you don’t have advanced programming skills, you can still create trading algorithms for cryptocurrency as well as other asset classes using programs and services that generate code based on your input. However, creating a successful algorithm involves several key steps, including understanding constraints, developing strategies, testing, and maintaining and updating your strategy.

Here are some of the key concepts to keep in mind at all times when creating, optimizing or testing a algorithmic cryptocurrency trading strategy.

Understanding Time, Financial, and Market Constraints

Before diving into algorithmic trading, it’s essential to consider the time frame and trading frequency you can monitor. While algorithms can operate independently, some human oversight is still advisable. Make sure to choose a trading frequency and time frame that fits your schedule and lifestyle.

Additionally, consider the financial constraints involved in algorithmic trading. High-frequency trading can generate significant commission costs, so it’s crucial to understand how your strategy will trade and ensure that you are mentally (and financially) prepared for it. Moreover, consider the starting capital required for different markets and financial products.

Not all markets are suitable for algorithmic trading, typically you’re looking for asset classes with high liquidity to support algorithmic order filling. Cryptocurrency, Forex, Stocks and ETFs are good candidates for it.

Strategy Development and Refinement

After understanding the constraints, focus on developing or refining a trading strategy that can be programmed. Rule-based strategies, which rely on quantifiable data or price movements, are the easiest to convert to an algorithmic trading strategy. Strategies involving entries, stop losses, and price targets are ideal for algorithmic trading.

Algorithm Testing and Evaluation

Testing is a crucial step in the development of trading algorithms. Before trading with real capital, run the algorithm on historical price data to evaluate its performance. If the historical testing phase is profitable and the algorithm meets your risk tolerance, proceed to test the algorithm in live conditions using paper trading. Æsir offers free paper trading for all algorithmic trading strategies.

Monitor the algorithm’s performance in real market conditions, as live trading can cause slippage and impact the market. Verify that the algorithm works as effectively in the real market as it did during testing.

Ongoing Algorithm Maintenance

It’s essential to monitor the algorithm’s performance and ensure it operates within the established statistical parameters. Refrain from tinkering with the algorithm, as doing so can undermine its emotion-free trading advantage. However, pay attention to market conditions and make adjustments to the algorithm if necessary.

In Conclusion

Algorithmic trading is not a get-rich-quick scheme but rather a method that requires time, effort, and planning. Be aware of the time, financial, and market constraints involved in your strategy and plan accordingly. Develop or refine a rule-based strategy, test its performance using historical and current data, and monitor its performance during live trading. Make adjustments as needed, but let the algorithm do its job once it has proven its effectiveness.